Reliable defect detection using multiple perspective scanning electron microscope images

ABSTRACT

A method for fast and reliable defect detection on semiconductor devices by comparing SEM images from a single perspective followed by a cross-check between at least two perspectives. An SEM equipped with at least two electron detectors each of which is capable of collecting electrons from different angular sectors. `Base` images of an area of the semiconductor wafer which is to be inspected are generated from both perspectives. For each perspective base image, a perspective `reference` image is generated, which is suitable for comparison with the base image. The reference image is registered with respect to the base image, for each perspective, the reference image is compared with the base image, and a comparison map of possible defect locations is produced, and, finally, a cross-check is carried out between the perspective comparison maps. The cross-check filters out events in the perspective comparison maps relating to variations other than defects such as pattern variations and noise.

This is a continuation of U.S. patent application Ser. No. 08/493,038,filed Jun. 21, 1995 now abandoned.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to a method for detection of defects onsemiconductor wafers using a scanning electron microscope (SEM) and,more particularly, to a method for fast and reliable detection ofdefects by comparing a number of SEM images, each of which containsdifferent information about the semiconductor device.

Defects on semiconductor wafers, such as particulate contamination, mayrender the wafers inoperative. Therefore, when manufacturingsemiconductors, a quality inspection is usually performed to detectdefects on the semiconductor wafers. Over the years, two mainapproaches--optical inspection and SEM inspection--have been developedand used to detect defects on semiconductor wafers.

Optical inspection of semiconductor wafers for defects detection isconsidered an effective and low cost method and is, therefore, the mostwidely used approach.

Various methods for detecting defects on semiconductor wafers usingoptical inspection equipment have been developed. For example, U.S. Pat.No. 4,805,123 to Specht et al., discloses a photomask and reticleinspection method and apparatus, wherein an examined surface area of agiven image is compared with a corresponding reference area.

Also known are various methods for defect detection using SEM basedequipment. For example, U.S. Pat. No. 4,794,646 to Jakeuchi et al.discloses an apparatus for detecting semiconductor wafer pattern defectswherein an inspected wafer area is compared to an image constructed frominformation, such as design rules, in a database.

As features on semiconductors become smaller than the wavelength ofvisible light, the size of defects which have to be detected falls belowthe resolution of conventional light optics. As a result, opticalinspection systems become increasingly unreliable. Furthermore, even ifthe defect can be detected with optical systems, the resolution is suchthat it is impossible to extract accurate additional information, suchas defect size and defect boundary. So the ability of optical systems toclassify defects is highly limited.

Scanning electron microscopes (SEM) are capable of resolving featuresmore than an order of magnitude smaller than the wavelength of visiblelight, and are, therefore, natural candidates for carrying out defectdetection and classification on these scales. In order for an SEM-basedsemiconductor wafer defect detection method to be feasible forindustrial purposes, the inspection must be fast and reliable and mustgenerate as few false alarms as possible.

However, since speed requires images to be generated employing a largefield of view, and since the faster electron microscope scanning iscarried out, the poorer is the image contrast-to-noise ratio, defectscovering small areas of the image are difficult to distinguish frompattern variations and noise, rendering fast scanning by existing SEMequipment, impractical due to the serious constraints on the imagequality produced.

As mentioned, for speed purposes, it is required to generate images in alarge field of view. This, in turn, means that defects covering only asmall fraction of the semiconductor wafer image are obtained. Therefore,the image of any given defect carries relatively little information,typically insufficient for purposes of identifying and characterizing itwith respect to the wafer pattern.

Rapidly acquired, large field of view, SEM image variations which arenot associated with semiconductor wafer defects are well known. There isa legitimate pattern variation between two ideally identicalsemiconductor wafer areas, which, at SEM resolution, using thecomparative approach described, may easily be interpreted as a defect. Asecond source of variations, not associated with semiconductor waferdefects, is variations characterizing the image formation processitself, such as noise or difference in focus. In both cases, the largerthe variation, the higher the potential for false alarm.

The source of contrast in SEM images depends, to a large extent, on theenergy range of the emitted electrons. For example, for backscatteredelectrons, contrast mostly reflects differences of material types.Although semiconductor wafers are made of a combination of materials,there is no guarantee that defects and wafer patterns are made ofdifferent materials. On the other hand, contrast produced by secondaryelectrons, emitted from the scanned object and having energy less than50 eV, depends almost entirely on surface topography. This contrast ismore suitable for semiconductor wafer defect detection.

The contrast-to-noise ratio for secondary electrons is inherently ratherlow but there are standard ways in which this ratio can be improved(see, for example, the book of L. Reimer, Image Formation in Low-VoltageScanning Electron Microscopy, SPIE Optical Engineering Press, 1993).

One way to improve the contrast-to-noise ratio associated with secondaryelectrons is to use detectors devised to collect secondary electronsemitted from the wafer which are scattered in a limited angular sector,rather than collecting them all. Explicitly, edges scattering electronsin the direction of the detector will be brightened while edges facingaway from the detector will be darkened. The effect of this shading isto greatly enhance image contrast. Secondary electron images formed fromcollecting secondary electrons in some limited angular sectors arereferred to herein as `perspective images`. Detectors collectingsecondary electrons of different angular sectors produce perspectiveimages carrying different information about the wafer pattern.

Perspective imaging improves topographic contrast, but it does nothingto overcome the problems of the prior art described above of SEMscanning in a large field of view and comparing variation betweenimages.

There is thus a widely recognized need for, and it would be highlyadvantageous to have, a fast and reliable method aimed at detectingdefects in semiconductor wafers based on a comparison of perspectiveimages and capable of filtering out large variations between comparedimages.

SUMMARY OF THE INVENTION

According to the present invention there is provided a method for fast,reliable defect detection on semiconductor devices by comparing SEMimages from a single perspective followed by a cross-check between atleast two perspectives.

According to further features in preferred embodiments of the inventiondescribed below, the invention requires an SEM equipped with at leasttwo secondary electron detectors each of which is capable of collectingelectrons from different angular sectors.

According to still further features in the described preferredembodiments, `base` images of an area of the semiconductor wafer whichis to be inspected are generated simultaneously from both perspectives.For each perspective base image, a perspective `reference` image isgenerated, which is suitable for comparison with the base image.

According to yet further features in the described preferredembodiments, for each perspective, the reference image is registeredwith respect to the base image, for each perspective, the referenceimage is compared with the base image, and a comparison map of possibledefect locations is produced, and, finally, a cross-check is carried outbetween the perspective comparison maps. The cross-check filters outevents in the perspective comparison maps relating to variations otherthan defects such as pattern variations and noise. As a result, acompleted comparison map is produced with significantly fewer falsealarms than the perspective comparison maps themselves.

The present invention successfully addresses the shortcomings of thepresently known configurations by providing a method for fast andreliable defect detection on semiconductor devices by comparing SEMimages from a single perspective followed by a cross-check between atleast two perspectives resulting in significantly fewer false alarms.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings, wherein:

FIG. 1 is a schematic depiction of a semiconductor wafer defectdetection apparatus according to the present invention;

FIG. 2 is a schematic depiction of an image processing flow according tothe present invention;

FIG. 3 is a schematic depiction of the registration process which is thefirst step in an image processing flow according to the presentinvention;

FIG. 4 is a schematic depiction of a pattern detection process which isthe first stage in a registration process according to the presentinvention;

FIG. 5 is a schematic depiction of an analyzing process which is thethird stage in a registration process according to the presentinvention;

FIG. 6 is a schematic depiction of a comparison process which is thesecond step in an image processing flow according to the presentinvention;

FIG. 7 is a schematic depiction of a comparison cross-checking processwhich is the third step in an image processing flow according to thepresent invention;

FIG. 8 is a schematic depiction of comparison maps obtained from twoperspectives and their completed comparison map generated during thecomparison cross-checking process which emphasize the ability of themethod of the present invention to differentiate defects fromsemiconductor wafer pattern variations .and noise produced during thescanning procedure;

FIG. 9 is a schematic depiction of the first stage of the comparisoncross-checking process, wherein dilation is applied to the completedcomparison map;

FIG. 10 is a schematic depiction of the second stage of the comparisoncross-checking process, wherein a list of candidate defects from thecompleted, dilated, comparison map is produced.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is of a method for fast and reliable defectdetection on semiconductor devices by comparing SEM images from a singleperspective, followed by a cross-check with one or more otherperspectives.

For ease of presentation, all further descriptions refer tosemiconductor wafers, it being understood that the methods of thepresent invention are suitable for other applications, including, butnot limited, to those mentioned above, all of which are intended to fallwithin the scope of the present invention.

The principles and operation of a method for fast and reliable defectdetection on semiconductor devices according to the present inventionmay be better understood with reference to the drawings and theaccompanying description.

Referring now to the drawings, FIG. 1 schematically illustrates anapparatus 20, for carrying out a semiconductor wafer defect detectionaccording to the present invention. The principal components ofapparatus 20 are a SEM 22, two secondary electron detectors, 24a and24b, each of secondary electron detectors, 24a and 24b, being orientedtowards a stage 26 on which an inspected semiconductor wafer 28 isplaced, and an image processing computer 30. Each of secondary electrondetectors, 24a and 24b, collects electrons from a different angularsector which is smaller than 180°.

Several different detector configurations are possible for generatingperspective images. One possibility is that the two detectors are both`external` to the SEM column and are placed so as to collect electronsfrom some limited angular sector. Another possibility is that the SEMitself is designed so that detectors can be placed in the SEM column, socalled `in lens` detectors. In this case two perspectives can begenerated by tilting the sample, having one `in lens` detector and one`external` detector. Yet another possibility is that two perspectivescan be generated by two `in lens` detectors. All the detectors so farmentioned have related to secondary electron emission. However, a twoperspective configuration can also be obtained with one detector forsecondary electrons and one detector for back scattered electrons.

For ease of presentation, in the method of the invention, as isdescribed herein, two secondary electron detectors are employed.Nevertheless, as will become apparent to those skilled in the art, morethan two secondary electron detectors may be used.

A semiconductor wafer includes an array of identical dies. A fraction ofa die is referred to herein as a wafer area, whereas a wafer area whichis to be inspected is referred to herein as a wafer base area. Since anygiven wafer area is supposedly identical to corresponding wafer areas ofany of the dies in a semiconductor wafer, any of the corresponding waferareas may serve as an inspected reference area for the inspected basearea. In a preferred embodiment, a single inspected reference area isused, which reference area is contained in a first die, which first dieis a neighboring die to a second die, which second die contains theinspected base area. Other embodiments of the method of the presentinvention make use of two or more inspected reference areas for eachinspected base area. Using more than one inspected reference areaincreases the degree of certainty regarding the presence of a defect andits location on the semiconductor wafer.

The semiconductor wafer 28 is placed on the stage 26 by a suitable waferhandling device 25. Stage 26 is then moved to a position which bringsthe wafer base area to be inspected, under SEM 22 electron beam 32, andtwo base perspective images of the wafer base area are generatedsimultaneously by detectors, 24a and 24b. Stage 26 is then moved to aposition which brings the wafer reference area to be inspected under SEM22 electron beam 32, and two perspective SEM images of the waferreference area are generated simultaneously by detectors, 24a and 24b.

The process of simultaneous image generation of a particular wafer area(either base or reference) by each of detectors, 24a and 24b, is carriedout as follows:

A fraction of the area (either base or reference) is scanned, and ananalog signal is transmitted from detectors, 24a and 24b to automaticbrightness and contrast control (ABCC) units, 38a and 38b, respectively,which then set the detectors gain and offset so that all images have amaximal dynamic range and differences in image intensity are reduced. Ina preferred embodiment, ABCC units, 38a and 38b are a standardcomponent, available, for example from Zeiss.

After the gain and offset of each of detectors, 24a and 24b, are set bythe corresponding ABCC units, 38a and 38b, the entire area (either baseor reference) is scanned, and an analog signal is transmitted fromdetectors, 24a and 24b, to a frame grabber 40 which is a component ofthe image processing computer 30. Horizontal and verticalsynchronization signals from the scanning unit of SEM 22 are alsotransmitted to frame grabber 40. The synchronization signals are used togenerate the perspective image obtained from each of detectors, 24a and24b, analog signals. Perspective images thus generated are stored in animage buffer unit 44 and await processing by the image processing unit46 of the image processing computer 30. Having generated at least oneset of base and reference perspective images in image buffer unit 44,processing by image processing unit 46 may begin.

The overall flow of data for image processing according to the presentinvention is shown in FIG. 2 and, as will be described in detail below,the image processing includes three main successive steps: (i) aregistration step; (ii) a comparison step; and (iii) a comparisoncross-checking step. As further shown in FIG. 2, the registration andcomparison steps are carried out on each perspective image of any givenreference and base areas, separately, whereas during the comparisoncross-checking step, information from both perspective images is crosschecked.

The flow of data during the registration step, which, as shown in FIG.2, is the first step of the image processing flow, is shown in FIG. 3.Registration herein refers to a registration correction process in whichdifferences between the base image and the reference image arecorrected. These differences are introduced because the images weregenerated from somewhat different locations on the semiconductor wafer,due to, for example, accuracy limitations of stage 26.

Furthermore, the registration correction process is also required tocorrect a locational misregistration, which is assumed to be acoordinate translation (Dx,Dy), limited by a search window havingdimensions of N×N. The dimensions of the search window, N×N, to a greatextent depend on the mechanical accuracy of stage 26 in a particularsystem being used. Misregistrations requiring a more complexregistration correction process, such as, for example, rotations, areassumed to be negligible.

As further shown in FIG. 3, the registration step is divided into threesuccessive stages: (i) a pattern detection stage, carried out by apattern detector 50; (ii) a correlation stage, carried out by acorrelator 52; and (iii) an analyzing stage, carried out by an analyzer54.

The flow of data through pattern detector 50 is shown in FIG. 4. Thepurpose of the pattern detection stage is to find templates of apredetermined size, `M×M`, in the reference image which containsufficient information to render them suitable for use as a basis forfinding the registration correction during the registration correctionprocess of the registration step. The flow of data through patterndetector 50 includes four successive steps:

(i) The first step of pattern detection, as delineated above, is tocalculate the local variance in a window having the dimensions M×M foreach pixel obtained from the reference image. This creates a varianceimage whose pixels are variance values reflecting the mount of patternin the M×M window, centered around the pixel.

(ii) The second step of pattern detection is a pattern significancecheck which includes carrying out a significance check for the existenceof sufficient pattern information to carry out registration by (a)normalizing the variance image with respect to a maximum variance; and(b) checking that a minimum variance is below a pattern significancethreshold. If the minimum variance is not below a pattern significancethreshold, an insufficient pattern is assumed, and a `no pattern signal`is set to `on`. Otherwise, the `no pattern signal` is `off`. The `nopattern signal` is passed, as shown in FIG. 3, onto analyzer 54 whichperforms the analyzing, third stage, of the registration step.

(ii) The third step of pattern detection is to find local maxima of thevariance image. Any given pixel is considered to be a local maximum ifits variance value is greater than, or equal to, the variances in a 5×5neighborhood of pixels centered around the given pixel. This creates animage whose pixel values are the local variance if the pixel is a localmaximum, and zero, if it is not. (iv) The fourth step of patterndetection is to choose `m` templates, wherein `m` is a fixed integer.The templates are preferably selected according to the largest localmaxima which define non-intersecting M×M templates, and are to be passedonto correlator 52, which performs the correlating, second stage, of theregistration step.

As further shown in FIG. 3, the second stage of registration isperformed by correlator 52. Correlator 52 calculates a registrationmatrix for each of the `m` templates defined by pattern detector 50. Tothis end, for each template, is defined an N×N search window, centeredon a pixel in the base image, which pixel corresponds to the center ofthe template. For each pixel of the N×N search window, a sum of absolutevalues of the differences between the template and the M×M windowcentered on the current pixel is calculated. This, in turn, generatesthe N×N registration matrix for each template. As shown in FIGS. 2 and3, and detailed in FIG. 5, the third stage of registration is performedby analyzer 54 and includes four steps:

(i) During the first step of the analyzing stage, analyzer 54 noteswhether the `no pattern signal` is `on` or `off`. If the `no patternsignal` is `on`, then the registration correction is set to (Dx,Dy)=(0,0) and the data are passed onto the comparison step, which, asshown in FIG. 2, is the second step of the image processing flow.

Otherwise, the analyzer carries out a registration matrix significancecheck for each of the `m` registration matrices. This includes: (a) tonormalize the entries of the registration matrices with respect to theirmaximum entry; (b) to assign a significance grade equal to thedifference between the normalized maximum and normalized minimumentries, to each registration matrix; and (c) to check for eachregistration matrix for which the minimum normalized entry is below aregistration significance threshold. If the minimum normalized entry isnot below the registration significance threshold, then the registrationmatrix is defined as too `flat` to produce a significant result and is,therefore, discarded. If all the `m` registration matrices arediscarded, registration for this perspective is deemed to have failed.In this case the `registration fail` signal is set to `on`. The`registration fail` signal is passed onto the comparison step, which, asshown in FIG. 2, is the second step of the image processing flow. If`registration fail` is `on`, the registration correction isautomatically set to (Dx,Dy)=(0,0).

(ii) During the second step of the analyzing stage, analyzer 54 is tofind a global minimum of each registration matrix that was not alreadydiscarded for being insignificant, as delineated above.

(iii) During the third step of the analyzing stage in which aperiodicity check is performed, analyzer 54 is to check for patternperiodicity by: (a) normalizing each significant registration matrixwith respect to the difference between the maximum and minimum entries(note that the `minimum` is the `global minimum` and its normalizedvalue will, therefore, be zero); (b) checking each of the registrationmatrices for local minima less than a predetermined `periodicitysignificance threshold`, wherein all of the registration matrix minimawith normalized values below the `periodicity significance threshold`are candidates for the registration correction process; and (c) labelinga registration matrix with more than one candidate for the registrationcorrection, as `periodic`, wherein if there is only one candidate, theregistration matrix is defined as `non-periodic`.

(iv) During the fourth and final step of the analyzing stage, analyzer54 is to calculate the registration correction. This is the only step ofanalyzer 54 which takes into account all of the significant registrationmatrices together. Candidates for the registration correction areconsidered to be equal if they are within +/-1 of each other. There arethree possible cases:

(1) If all the registration matrices are `non-periodic`, analyzer 54 isto count the number of times each candidate appears. The most frequentlyappearing candidate is the registration correction.

(2) If there are both `periodic` and `non-periodic` registrationmatrices, analyzer 54 is to create a list of candidates from thecandidates appearing in the `non-periodic` registration matrices andcount the number of times each candidate in the list appears among allthe registration matrices (both `periodic` and `non-periodic`). In acase where each of the candidates appears only once, analyzer 54 is todiscard the `non-periodic` registration matrices, and to set the case tobe that of `periodic` registration matrices only (see below). Otherwise,the most frequently appearing candidate is taken as the registrationcorrection.

(3) If there are only `periodic` registration matrices, analyzer 54 isto count the number of times each of the candidates appeared. The mostfrequently appearing candidate is the registration correction.

If no candidate appears more than once, the registration correction istaken from the registration matrix that received the highestsignificance grade in step (1) of the analyzer.

Having completed the registration step of the image processing flow, asshown in FIG. 2, the second step of image processing flow according tothe method of the present invention, the comparison step, is carriedout. As for the registration step, the comparison step is carried outfor each perspective separately. During the comparison step a binarycomparison map with the same pixel dimensions as the base image isproduced.

As shown in FIG. 6, the comparison step includes three stages: (i)During the first stage of the comparison step, the registrationcorrection coordinates (Dx,Dy) are applied to the reference image. (ii)During the second stage of the comparison step, an edge detectionprocess, on both the reference and base images is performed. The edgedetection process produces binary reference and base images as follows:a standard Laplucian operator is applied to each pixel of the image.Whenever the value of the standard Laplucian operator exceeds a fixedpredetermined edge detection threshold, the binary output pixel is setto `1`, otherwise, the binary output pixel is set to `0`.

(iii) Comparison is completed by comparing the binary reference and baseimages. If the registration fail signal is `on`, the comparison map isset to zero. Otherwise, the binary comparison map is a logical`exclusive or` of the reference and base area images. That is, wheneverthere is a `1` in the reference area image and a `0` in the base areaimage, or vice versa, the corresponding pixel in the comparison map isset to `1`. Otherwise, the pixel in the comparison map is set to `0`.Another configuration in which the comparison map can be created is byoperating directly on the base and reference area images (as opposed totheir corresponding binary edge images). In this configuration, theabsolute difference of the base and reference area images is calculated.For every difference exceeding a threshold value, a `1` is placed in thecomparison map, whereas, for every difference not exceeding thethreshold value, a `0` is placed in the comparison map.

As shown in FIG. 2 and detailed in FIG. 7, the third and final step ofthe image processing flow for semiconductor wafer defect detectionaccording to the present invention is the step of cross-checking betweenthe two different perspectives. It will be recalled that the aim ofcross-checking is to exploit different information present in eachperspective, to increase the reliability of defect detection in thepresence of, for example, noise and pattern variation.

As shown, for example, in FIG. 8, noise 64 is a random phenomenon, sothat the likelihood that noise occurs in different perspectives in thesame pixels is extremely small. By cross-checking data from differentperspectives it should be possible to eliminate false alarms caused bynoise.

As further shown in FIG. 8, pattern variation is not random, but, in thecomparison maps of each perspective `1` and `2`, appears as differences62 in the edge information between the base area and the reference area.For a given feature 63, different edges are emphasized in differentperspectives. So the pattern variation 62 associated with differentedges appears in different perspective comparison maps. Furthermore,when perspective comparison maps are overlayed, events associated withpattern variation in different edges will be separated by the featurewidth.

This, however, is not the case for defects 61. While events 60associated with defects 61 may appear in different perspectivecomparison maps, when the maps are overlayed, they will tend to berather close to each other. Therefore, the cross-checking processprovides a means for separating noise and pattern variations fromdefects.

As shown in FIGS. 7-10, the cross-checking process includes three majorstages:

(i) The first stage of the cross-checking process includes: (a) eachpixel of the perspective comparison maps is multiplied by a fixed valuethat can be used to identify the perspective. For example, each pixel ofperspective `1` comparison map is multiplied by `1`, and each pixel ofthe perspective `2` comparison map is multiplied by a `2`; the twoperspective comparison maps are combined into a single completedcomparison map by applying an `exclusive or` operator. Note that pixelsin the completed comparison map can take the values `0`, `1`, or `2`;(b) A morphological dilation operator is applied to the completedcomparison map. Any pixel with a non-zero neighbor is given a non-zerovalue. As shown in FIG. 9, the value is set according to the majorityvalue amongst the non-zero neighbors. If there is no majority, the pixelis given the highest value of its non-zero neighbors. The dilationoperator can be applied one or more times.

(ii) The second stage of the cross-checking process, as shown in FIGS. 7and detailed in FIG. 10, is to produce a list of candidate defects fromthe completed, dilated, comparison map. A candidate defect is any set ofcontiguous, non-zero pixels in the comparison map. Candidate defects arebuilt up as follows; Starting in the top-left corner of the completedcomparison map, for each non-zero pixel there are three cases:

(1) The new non-zero pixel can be connected by a contiguous set ofnon-zero pixels to a single existing candidate.

(2) The new non-zero pixel can be connected by contiguous sets ofnon-zero pixels to more than one existing candidate defect.

(3) The new non-zero pixel can not be connected to any existingcandidate defects.

In case (1) above, the new non-zero pixel is added to the candidatedefect. In case (2) above, all the candidate defects connected to thenew non-zero pixel are combined into a single new candidate defect. Incase (3) above, a new candidate defect is created.

(iii) The third stage of the cross-checking process is to decide whichcandidate defects are actually defects. Any candidate defect having morethan a fixed threshold number of pixels from each perspective, is saidto be a defect. The fixed detection threshold is greater than one. Themethod of the cross-checking process described above, requires thatdefects appear in both perspectives. Noise and pattern variations arefiltered out from defects because, as stated with reference to FIG. 8above, events in perspective comparison maps that are associated withdefects tend to be far closer together than events associated with noiseor pattern variation. So, a dilation operation will combine events indifferent perspectives associated with defects before it combines eventsin different perspectives associated with noise or pattern variation.

(iv) The final step of the comparison cross-checking process is tocalculate attributes of the defect that can be used for classification.

The most basic attribute is the defect location which is set to be thecenter of gravity of the constituent pixels of the defect. Otherexamples of attributes are defect size, the number of pixels making upthe defect, and defect boundary, the convex hull of the pixels making upthe defect. The method of comparison followed by cross-check betweenperspective images can be exploited to calculate yet further attributesof defects such as whether the defect is above or below the waferpattern. All these attributes can be used subsequently for defectclassification.

The cross-checking process, described above, required a defect to appearin both perspective images. Another possibility for cross-checking is torequire that defects appear in any one perspective comparison map. Thismakes it possible to detect defects in a depression that are hidden inone of the perspectives. In this case, defects have to be distinguishedfrom noise and pattern variation by other characterizations than thosedescribed above. A minimum size requirement on defects will distinguishdefects from noise and, to a lesser extent, pattern variation. Defectswill be further distinguished from pattern variation by using propertiesof the pattern that are not true for defects. For example, the degree oflinearity of the pattern is greater than that of the defect (see FIG. 8,and its accompanying description above). So a measure of linearity willhelp to separate defects and pattern variation.

To conclude, the method of the present invention is capable ofdistinguishing between a semiconductor wafer defect, on the one hand,and semiconductor wafer pattern variation or noise introduced during thescanning procedure, on the other. The ability of the method of thepresent invention to reliably distinguish between defects and patternvariations and/or noise is primarily due to the generation of a separatecomparison map for each of the two perspectives employed, followed bygenerating a completed comparison map from the comparison maps of theindividual perspectives. The ability of the method of the presentinvention to reliably distinguish between defects and pattern variationsand/or noise, is a result of the completed comparison map being producedwith significantly fewer false alarms than the perspective comparisonmaps themselves.

While the invention has been described with respect to a limited numberof embodiments, it will be appreciated that many variations,modifications and other applications of the invention may be made.

What is claimed is:
 1. A method of detecting defects in objects, such assemiconductor wafers, which include an array of ideally identical parts,the method comprising the steps of:(i) inspecting a base area and atleast one corresponding reference area of an object which includes anarray of ideally identical parts, each of said areas being inspectedfrom a plurality of angular sections thereby forming a base perspectiveimage for each of said plurality of angular sections and a correspondingreference perspective image for each of said plurality of angularsections, respectively, wherein said inspection is performed by ascanning electron microscope apparatus including at least two detectors,thereby generating said base and said corresponding referenceperspective images; (ii) for each of said perspectives, comparing saidbase perspective image and said corresponding reference perspectiveimage, and, for each of said perspectives, creating a perspectivecomparison map; and (iii) if a difference is indicated, cross checkingsaid comparison maps created for each of said perspectives.
 2. A methodof detecting defects in objects, such as semiconductor wafers, whichinclude an array of ideally identical parts, the method comprising thesteps of:(i) inspecting a base area and at least one correspondingreference area of an object which includes an array of ideally identicalparts, each of said areas being inspected from at least two angularsections thereby forming a base perspective image for each of said atleast two angular sections and a corresponding reference perspectiveimage for each of said at least two angular sections, respectively,wherein said inspection is performed by a scanning electron microscopeapparatus including at least two detectors, each of said detectorsdetecting electrons from a predefined angular section which is smallerthan 180° and thereby generating said base and said correspondingreference perspective images; (ii) for each of said perspectives,comparing said base perspective image and said corresponding referenceperspective image, and, for each of said perspectives, creating aperspective comparison map, said perspective comparison map indicatingdifferences between said base perspective image and said correspondingreference perspective image; and (iii) if a difference is indicated,cross checking said comparison maps created for each of saidperspectives, thereby indicating substantially solely of presence of adefect and a location of said defect on said inspected object.
 3. Themethod of claim 2, further comprising the step of processing aregistration correction, said registration correction process precedessaid comparison maps said registration correction process serves tocorrect differences between said base perspective image and saidcorresponding reference perspective image for each of said at least twoangular sections, said differences introduced because said images weregenerated from different locations on said object.
 4. The method ofclaim 3, wherein for said base perspective image and said correspondingreference perspective image of each of said at least two angularsections, said comparison maps produce binary comparison maps, saidbinary comparison maps reflect differences between said base perspectiveimage and said corresponding reference perspective image, for each ofsaid at least two angular sections.
 5. The method of claim 2, whereinfor said base perspective image and said corresponding referenceperspective image of each of said at least two angular sections, saidcomparison maps produce binary comparison maps, said binary comparisonmaps reflect differences between said base perspective image and saidcorresponding reference perspective image, for each of said at least twoangular sections.
 6. The method of claim 2, wherein said base area iscompared with a single corresponding reference area, said base andreference areas being each inspected from first and second angularsections to generate first and second base perspective images and firstand second reference perspective images, respectively.
 7. The method ofclaim 6, further comprising the step of processing a registrationcorrection, said registration correction process precedes saidcomparison maps said registration correction process serves to correctdifferences between said first base perspective image and said firstreference perspective image, and differences between said second baseperspective image and said second reference perspective image, saiddifferences introduced because said images were generated from differentlocations on said object.
 8. The method of claim 7, wherein for saidfirst base perspective image and said first reference perspective image,said comparison maps produce a first binary comparison map, said firstbinary comparison map reflects differences between said first base andreference perspective images, and for said second base perspective imageand said second reference perspective image, said comparison mapsproduce a second binary comparison map, said second binary comparisonmap reflects differences between said second base and referenceperspective images.
 9. The method of claim 6, wherein for said firstbase perspective image and said first reference perspective image, saidcomparison maps produce a first binary comparison map, said first binarycomparison map reflects differences between said first base andreference perspective images, and for said second base perspective imageand said second reference perspective image, said comparison mapsproduce a second binary comparison map, said second binary comparisonmap reflects differences between said second base and referenceperspective images.
 10. The method of claim 6, wherein each of saiddetectors is selected from the group of detectors consisting ofdetectors which are external to said scanning electron microscopeapparatus and detectors which are internal to said scanning electronmicroscope apparatus so called `in lens` detectors.
 11. The method ofclaim 6, wherein each of said detectors is selected from the group ofdetectors consisting of detectors detecting secondary emitted electronsand detectors detecting back scattered electrons.
 12. The method ofclaim 6, wherein said cross checking is exploitation of differences ofedges information present in each of said perspective comparison mapsand generates a completed comparison map, said completed comparison mapindicates substantially solely of presence of a defect and a location ofsaid defect on said inspected object.
 13. The method of claim 6, whereinsaid cross checking is exploitation of features unique to said defects,and said features are selected from the group of features consisting ofsize and shape.
 14. The method of claim 2, wherein each of saiddetectors is selected from the group of detectors consisting ofdetectors which are external to said scanning electron microscopeapparatus and detectors which are internal to said scanning electronmicroscope apparatus so called `in lens` detectors.
 15. The method ofclaim 2, wherein each of said detectors is selected from the group ofdetectors consisting of detectors detecting secondary emitted electronsand detectors detecting back scattered electrons.
 16. The method ofclaim 2, wherein said cross checking is exploitation of differences ofedges information present in each of said perspective comparison mapsand generates a completed comparison map, said completed comparison mapindicates substantially solely of presence of a defect and a location ofsaid defect on said inspected object.
 17. The method of claim 2, whereinsaid cross checking is exploitation of features unique to said defects,and said features are selected from the group of features consisting ofsize and shape.